Someone referred to it as the permanent underclass. The conversation got quiet because this outcome is plausible, and many believe it is probable.

Tech moves in platform shifts

Each wave compresses the gap between innovation and displacement. Policy is always reactively designed by design — always one cycle behind. How could it be any other way? You can’t regulate your way into innovation - it’s a one-way street.

The provocation

In the era of Andrew Yang, UBI was discussed as a floor like welfare.

But now we are negotiating the terms of irrelevance. How might we buyout anybody in the way of our ability to action progress as we define?

This is a choice embedded in how we think about progress.

THE PARADOX

Leverage concentrates yet it also democratizes. Progress can be mutually beneficial while the gains remain unevenly distributed.

Technology creates winner-take-most dynamics. Some people have access to leverage on better terms than the rest of us.

Two siblings with AI can IPO at a $1T valuation while displacing more than a thousand $1B companies.

But

Technology also expands TAM1. It enables niche value at scale. Pre-internet, we needed mass-market economics to justify infrastructure. Shopify is enabling more than 100,000 shops doing $1M in annual sales. Tether’s profit is estimated at $30M per employee.

We’re not in a zero-sum collapse, but we are are in an expansion that looks like displacement. Our institutions offer and reward credentials that were designed for a post industrial-era. The Four-Hour Work Week was published less than 20 years ago.

Today, some of us don’t bat an eye when someone talks about 4-hours of vibes2 resulting in recurring revenue.

FORWARD ≠ UPWARD

The panic around AI displacement assumes everyone needs to climb the same ladders.

Career
  advancement ≠ 
 
Economic
  output ≠

Linear
  progression ⛔️

The inequality of outcomes is negative framing. Another way of explaining human progress as multi-dimensional flourishing.

Economic flourishing may continue to be the most quantifiable axis but it is not the only one. AI is a technology of leverage and as such it can assist us to lever-up our individual micro-economic activity. Again, some people will be able to access the leverage on better terms.

But

If we lever-up we can attain more Psychological flourishing. Securing economic safety can help many of us move from emotional dysregulation to regulation.

If we lever-up we can seek more Spiritual and Intellectual knowledge. We can connect some dots for the first time and almost all dots almost instantly. The opportunity lies in knowing what dots to ask for. More answers await us than we will have time on the Earth to find.

If we lever up we can build Relational coalitions. We can connect with and contribute to exponential communities in a meaningful way because of our non-human derivatives (AI agents).

In a world of expanding TAM and niche-value efficiency, we don’t need everyone on the same path. We should encourage each other to find our unique versions of forward.

But

Are we heading towards paying people to stay out of the way? Or are we creating optionality for them to find non-traditional paths to agency?

COLLABORATIVE ECONOMICS

Moving forward together rather than maintaining a winner-takes-all mentality will help us navigate the abundance that we will harness with AI. Right now it looks like chaos for most of us.

Because of the pace of change, new paths to flourishing are emerging faster than cultural narratives can absorb them. Cultivating a high-fidelity network will be the way to see opportunities: have you noticed how Linkedin is now commonly sourced by AI? The AI is leaning into bigging-up, us humans.

We need to accept that transactions drive reciprocity. We need people to want us to succeed because our success expands their network’s surface area.

Markets are fragmenting.

Hoarding zero-sum wins forces us to defend our position constantly. If we’re building coalitions, we’re positioned as a connectors. And connectors are highly valuable in fragmented markets, especially.

Helping others succeed expands the TAM of your own network, which means more collective opportunity. Just as AI is probabilistic, so is reciprocity. When we collaborate someone pays it forward, but not necessarily back to you.

From an energy perspective it is generative because if we decide we are on the same team then shared entropy results in less waste. When we play defense, collective energy depletes over time.

The uncomfortable question

What about people who can’t or don’t join coalitions?

The socially or technically
  isolated 

The neurodivergent
  who struggle with networking

Communities where trust
  has been destroyed.

THE DESIGN CHOICE

We’re at a fork where each of us can decide for ourselves to pursue mutual advancement or a Hunger Games-esque fight to the finish. We get to decide how to recognize multiple paths to flourishing. Right now, it feels like we are being drawn towards a future in which it will pay to pay people to stay out of the way.

And

If we default to paying people to get out of the way, we guarantee that outcome. If we design for coalitions and multidimensional flourishing we increase the probability for different possibilities.

AI will displace jobs. It just will. Hopefully we can build a world where displacement means new opportunities rather than “you’re irrelevant”.

Music: Some Places Blue Hawaii Chop-n-Screw by Grapell and Blue Hawaii

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Every time you turn me down
I tell myself it’s the last time
That I wait for a change
Cause you always turn away

I’ve lived 90% of my life feeling inadequate compared to people who’ve spent their time grinding 10,000 hours at a single task. I’m impressed by those who have done this: Tiger Woods, The Beatles (Hamburg sessions) and so many of my University+ friends who have been successfully working for years in their specific domains.

But grinding towards a single task is not what makes life feel vibrant and generative for me.

I operate from a point of being inspired. I get excited about ideas across different domains. As a child of the 80s who grew up 20 miles from the Bronx, I like sampling, remixing and nodding to those who came before.

My persistent tension: how do I make progress when my default is to wait for inspiration?

Inspiration is my magnet.

It creates velocity but not always direction, which comes from Order.

I get excited by novelty. Neither inspiration or order is morally superior yet they’re different ways to pay the inevitable cost of being available.

Inspiration and Order are different approaches to prepaying or postpaying for outcomes.

You always have to pay something. The question is whether you want to be more in control over how you pay, or whether you prefer to go with the flow and accept the price when it arrives.

People driven by order manufacture control to reduce uncertainty. The Order-prone need checklists, schedules and a predictable cadence to feel safe enough to act.

Inspired-first people sometimes feel this predictability like a straightjacket that mutes possibility: I experienced it for far too long without knowing how to change clothes.

Both sides have blindspots. The inspired can defer indefinitely under the guise of “waiting for the right time,” while the orderly can mistake activity for progress.

As I previously wrote activity is not the same as progress. Many people “work their ass off” yet never set their own progress objectives. They confuse busyness with forward movement.

Being proactive by setting learning objectives and owning outcomes is how you convert bursts of inspiration into durable results. For people who prize order, I believe the lesson is to lean into ambiguity, especially when you are in trusted company.

Order without space to discover turns into pointless routine; inspiration without discipline leads to strings of unfinished experiments.

So what does a middle path look like in practice?

Music: Some Places by Grapell and Many Voices Speak

What's going on in Iran?

I stopped at the AB, short for AB Vassilopoulos - Greece’s second largest grocery store chain, to pick up some food before continuing left to my final destination.

One of my low-key joys is visiting foreign supermarkets to stock up on yum-yums: random local delicacies that locals take for granted. I did a quick 10-minute stock-up at AB but left a teeny bit disappointed not to have found anything terribly special. In fairness it was a mini AB, but still. Everything looked pretty much the same as what I am used to in New York.

Last night, I did some research on supermarkets. Perhaps unsurprisingly, I confirmed my intuition that the biggest grocery systems are increasingly controlled by a relatively small set of multinational groups.

Food retail is a strategic system. As we’ve seen in recent times, when you stress a country with pandemics and war, the state suddenly cares deeply about supermarket logistics and pricing power.

Supermarket margins are thin. The leverage is in scale and supply chain control. Walmart’s business model is all about making a tiny margin per product but selling an enormous volume across thousands of stores.

I asked my AI about the major chains in Greece and sure enough, Alfa-Beta is owned by Ahold - the publicly traded Dutch-Belgian company that also owns 100% of Stop & Shop and Giant in the USA. Ahold also owned Fresh Direct before selling it to Getir a few years ago. No wonder everything looked familiar to me.

But guess what? The #1 supermarket chain in Greece is a family dynasty!

Sklavenitis is #1

How have they been able to hang on to it amidst the global trend of multinationals that own the top supermarkets? I fed my conspiracy theory to AI: I think food retail must be the most important low-margin business in existence, because if you control the food, you control whether or not the people eat. In my Orwellian frame of mind, I feel like this is an industry that governments proactively prevent individual families from controlling.

Is there any merit to this?

So in Greece, the story is almost inverted: the Sklavenitis family group rescued a brother-in-arms in 2017: the Marinopoulos family, who themselves bought out their former JV multinational partner Carrefour in 2012. In fact 8 of 10 top supermarkets in Greece are family owned. The country’s fragmented geography is composed of islands and mountains, which rewards local know-how and relationships.

So despite low margins and high fixed costs, the Greeks are still mostly keepin’ it in the fam! Long live anti-Walmartization!

Old money dynasts

Europe is well known for dynasties1. Some of them include:

• The Agnelli family in Italy still controls Exor, which is the primary shareholder in Stellantis (Jeep, Ram, Dodge, Chrysler, Fiat, Peugeot, Citroën), Ferrari, and other industrial and media assets.

• Sweden’s Wallenberg family, via Investor AB, owns AstraZeneca, Electrolux, Saab, Ericsson and others. They have shaped Swedish industrial policy for over a century.

• The Wendel family in France turned their old steel empire into an investment holding that still owns large stakes in major industrial and services companies

Not to be outdone, let’s not forget Asia and Latin America.

• Korea: Samsung (Lee family)

• India: Reliance Industries (Ambani family)

• A variety of Hong Kong and Southeast Asian groups remain controlled by founding families while being among the largest firms in their markets.

In parts of Asia and Latin America, estimates2 suggest the majority of $1B+ companies are still family‑run. America is very different.

New money dynasts

The big American families such as the Rockefellers and Vanderbilts persist as philanthropy brands, but their descendants no longer have direct control of the great oil and steel companies they used to rule.

J.P. Morgan is a GSIB - Global Systemically Important Bank - a quasi government operation. The J.P. Morgan heirs have no special voting block or dynastic command over the bank’s strategy. In fact, American corporate governance is known for one‑share = one‑vote. It has the strongest capital markets with the most active mergers and acquisitions activity. This perpetuates fast recycling of control. America is also the world’s largest consumer economy so perhaps descendants of dynasts share correlation with spend-thriftiness.

Modern American technologists are emergent dynasts. Founder‑CEOs such as Mark Zuckerberg leverage super-voting shares to maintain majority decision making control even as their equity interests get diluted.

Jeff Bezos doesn’t own a special share class, but he maintains a large ownership position and executive influence.

If you are in tech then you know all about the Paypal Mafia.

Today’s American dynasties seem to be designed into the cap table.

AI increases the probability that control might pass through whoever owns and steers the key models, data, and infrastructure.

Dynasties may be defined by enduring control over the technical and institutional systems that shape how everyone else thinks, works, and coordinates.

It sounds Orwellian to me.

Music: Riding The Chicky Nuggie Train by Baby Yoda

Think of a 340‑slide deck you’ve been patching for 25 years.
Every new trend gets a new slide, copied charts, slightly different numbers, and by the end, it becomes difficult to digest.

Refactoring is the painful weekend where you merge duplicates, standardize the logic, and label everything so a normal human can find slide 27 next quarter.

In AI land, we’re doing the same thing, but not just to code.
We’re refactoring the conversations we have with large language models.

Prompt engineering is not limited to clever wording. The real game is how you structure the system prompt because this context is sent and re-sent to the model with every back-and-forth chat session.

The system prompt is the long, mostly invisible instruction block that tells the model who it is, what it can do, what tools it can call, and how you want answers formatted.

Every word you send to the model is broken into tokens, and you pay for tokens the way you pay for cloud compute or API calls.

Bloated prompts and oversized context windows translate directly into higher unit costs and slower responses.

Bad prompt design is a new kind of technical debt.

In the old world, code refactoring had a simple definition: change the internal structure without changing the external behavior.
From the business side, the benefits were clear: cheaper to maintain, easier to add features, fewer nasty surprises in production.

In the new world, “prompt refactoring” and token optimization follow the same rule.

It is important to re-write how you talk to the model so that:

• The model behaves the same (or better) on real tasks.

• You send fewer tokens per request.

• You avoid dragging useless history and documents into every single call.

Try these moves:

Ultimately this is first principles business logic.

Look for the smallest, cleanest interaction that still gets the job done. Keep in mind that every extra non-additive instruction in your system prompt costs money (tokens) and makes your app slower because the AI must process more tokens.

Refactoring software is a technique, not a phase: small, continuous improvements to keep complexity from running away from you.

Prompt and token refactoring will be the same. As your AI use cases grow, you’ll keep revisiting how you structure instructions, what context you send, and which model you choose for which job.

For business leaders: you don’t need to learn to code, but you should get comfortable asking how your team is structuring its conversations with AI, not just what features they’re building.

A surprising amount of margin and reliability lives inside these invisible blocks of text.

Today, I refactored the system prompt for PitchDeck which uses AI model: Claude Sonnet 4.6 at a cost of $3.00 per 1M input tokens and $15.00 per 1M output tokens.

My app has an average session length of 5 turns and today I cut 50% of my system prompt by consolidating instructions and eliminating redundancy (refactoring). Now my system prompt is approximately 13,500 tokens x 5 turns = 67,000 tokens saved per session!

Key takeaways

Refactoring is when you restructure internal logic (code or prompts) while keeping or improving external behavior.

This is my analogy - your mileage may vary - but I think it is directionally accurate.

When refactoring your goal is to:

1. Minimize cost

2. Minimize time it takes your app to respond

3. Minimize sprawl - the messier your code / prompts the more difficult it becomes to fix things later on, just like finding a needle in a haystack

Music: Animals Don’t Lie by SRNDE

If you want to use Chipp my preferred “AI Wrapper” this link will get you 10% off for six months. Feel free to DM me and I can help you onboard. I promise you that I can help you not only make but sell an AI app in less than 24 hours.

Disclosure: I’m an investor in Chipp.

Yesterday, while mowing the lawn I was thinking about how I was taught to create economic value. How does value actually move between people? We usually talk about “trickle down” or “bubble up” as if value can flow only in one direction, governed by hierarchy, gravity or energy dependence. Reflecting on my valuable highlights, I remember them as collisions rather than trickles.

Two people meet. They talk. One has a capability, the other has a need. Then they transact their time, attention, expertise, capital, whatever. Something new gets created that didn’t exist before. Then they go collide with other people, and those collisions create more value.

Sometimes days, months and usually years later an overnight celebrity is born: a beneficiary of compounding returns that have nothing to do with hierarchy and everything to do with who they collided with, when they met and what they built together.

I’m calling this collision economics, and it’s built on a premise that makes people uncomfortable: transactions build relationships, not the other way around.

The Relationship Ledger

I think the consensus view is that you are bequeathed certain relationships through shared history, emotional connection, family ties, whatever… and then you MUST transact within the expected norms of such relationships.

Expectations, expectations and more expectations.

If we want relationships to compound value over time, we must value every interaction as a deposit or a withdrawal.

Accounting matters. Check out Part 1 for more on how relationships are ledgers. You don’t get a strong relationship just because you slap a label on it: “friend,” “partner,” “colleague.”

You earn it, reinforce it, or destroy it, one transaction at a time.

If you want better relationships, you need better transactions. And if you want compounding value, you need to be intentional about who you’re transacting with.

The Zero-Sum Training Problem

This is hard because most of us were trained for the wrong game (myself included). If you went through elite education, competitive hiring, venture-backed startups, or any other “winner-take-all” system, you were taught to compete, not collaborate.

Blitz-scale.

Move fast and break things.

To the victor belong the spoils.

PUMP and dump your bags on the public.

I know this because I lived it. I succeeded on that hamster wheel. I’m grateful for the experience and humble about the privilege that got me there.

And I also know what that training cost: it taught me to see other people as competitors, not collaborators. It taught me to optimize for individual extraction, not mutual value creation.

If there’s a fixed pie and only one person can win, then yeah, be mercenary about it. But most of life isn’t actually zero-sum. Most value creation happens when smart people collide and build something together that neither could have built alone.

The problem is we’re playing the positive-sum game of life with zero-sum training. Remaining in this mindset leaves enormous amounts of value on the table.

The Collision Opportunity

What if we designed systems and trained ourselves to maximize high-quality collisions instead of individual extraction?

Not kumbaya bullshit about “everyone wins.” I want intentional, reciprocal, value-creating transactions between people who are actually capable of delivering.

It’s transactional commerce that is commercial, focused on creating and capturing value. BUT it’s also collaborative and designed for mutual benefit, compounding returns, and long-term reciprocity.

The pie can almost always be bigger with collaboration.

The people who figure out how to systematically engineer generative collisions will extract more alpha than the zero-sum mercenaries. I’m trying to figure out this superior form of game theory.

The most lucrative and enjoyable parts of my career have come from collaborative collisions, not competitive extraction. Every successful entrepreneur I know who’s built something sustainable did it by being in service of customers and collaborators, not by grinding competitors into dust.1

The Filter Problem

Not all collisions are created equal.

Some people are generative. We create more value than we capture. We show up with quality transactions, we reciprocate, we compound value over time.

Other people are extractive. We take more than we give. We’re charming, we’re competent, but we’re fundamentally parasitic. We’ll drain your ledger and move on.

Most people are in-between—people. We are conflicted; we have the capability to be generative but are stuck in zero-sum training, or who are one bad incentive structure away from becoming extractors.

Collaboration frameworks like the ones I used to use at IDEO, assume everyone is equally generative, or that good intentions are enough, or that “building relationships” will magically filter out the parasites. But it doesn’t work that way.

If you design collision systems without filtering for creators, you’re building a buffet for parasites. The extractors will show up, take what they can, and leave. The creators will get burned and retreat. And you’ll be left wondering why collaboration doesn’t work.

Where Are You on the Matrix?

I’ve been thinking about this through the lens of The Fountainhead — a book most of my friends have never read and many have negative impressions of. The book’s characters serve as a useful framework for understanding human archetypes:

Dear reader, where are you on that matrix?

And more importantly: who are you transacting with?

If you’re a creator transacting with other creators, you’re compounding value with every collision.

The Challenge

Pick someone you interact with regularly and assess the quality of your mutual transactions. Are you both adding value to each other or is it asymmetric?

Is this relationship compounding or depleting?

Are you transacting with someone who is a creator, parasite, conflicted, or broken?

What archetype are you when you’re with them?

What would it take to move one square up or to the right?

We can’t “fix” the other person. “You can’t control me,” as my daughter says. But what would it take for you to show up with higher transaction quality?

What’s Next

Transactions build relationships, collisions create value, and we need to be intentional about both the quality and the counterparty.

Next, I’m exploring a methodology about how to filter for creators, how to design economic collisions that compound, and how to build them into your actual practice.

Longer-term I’m looking for collision partners to discuss the economics of reciprocity. What systems and platforms are people using to succeed? Who is making collision economics work for them?

Let me know if any of this resonates. How easy or difficult is it for you to audit your transactional ledger or assess where you sit on the The Fountainhead Matrix?

Leave a comment

Music: Overnight Celebrity by Twista ft. Kanye West and Miri Ben-Ari

On May 1, 2026, the United Arab Emirates officially withdrew from OPEC after 59 years of membership. Nine days earlier, on April 22, Treasury Secretary Scott Bessent publicly acknowledged that the UAE had requested a currency swap line with the United States, stating it would “benefit both the UAE and the United States.”

The petro-state ranked #1 for sovereign wealth asked for access to emergency dollar liquidity one week before leaving a six-decade-old cartel. And the U.S. said yes.

This is a business deal. And you know who likes to deal. The architecture of global finance is being re-written in real time.

The Facts

Under OPEC quotas, the UAE has been producing roughly 30 percent below capacity. Approximately 3.4 to 3.8 million barrels per day against infrastructure capable of 5 million bpd by 2027.

Based on current prices ~$100 / barrel, adhering to OPEC’s cartel politics equated to over $150 million in lost revenue per day!

And the UAE was already cheating: data shows they exceeded their OPEC production quota in 69 percent of months between 2017 and 2022: they were the kid in class who keeps getting caught passing notes.

The Swap

One week before the OPEC exit, Treasury Secretary Bessent confirmed that the UAE’s central bank governor had approached him about establishing a Federal Reserve currency swap line.

This is either absurd or brilliant, depending on your perspective:

The UAE holds 2.7 trillion in sovereign wealth funds.
The UAE is running a 5% fiscal surplus.
The UAE is one of the wealthiest nations per capita on earth.

A Fed swap line is a credit facility that gives foreign central banks access to emergency dollar liquidity.

Historically, the Fed has extended swap lines to:

1. Central Banks (Bank of England, ECB, Bank of Japan)

2. Emerging markets facing acute crisis (Brazil, Mexico, South Korea during COVID-19)

The UAE is neither. It’s not systemically important to U.S. financial stability, and it’s not in crisis.

If the US ends up extending a swap line to the UAE, you can think of it like Jeff Bezos applying for and receiving a payday loan.

The Timing

The UAE had been in OPEC since 1967. The cartel defined UAE energy policy for six decades and this is a country that successfully implements “30-year-plans” - I know because I played a small role establishing what was then known as the Abu Dhabi Media Zone Authority.

Major strategic pivots like this are planned years in advance and coordinated with key partners.

The U.S. Treasury Secretary publicly endorsed a swap line arrangement nine days before the exit.

The Warsh

During Senate confirmation hearings, Fed Chair nominee Kevin Warsh was asked about the UAE swap line. His response should have been front-page news, but it got buried in the usual Fed-speak:

On April 29th, Warsh testified that the Federal Reserve is not fully independent when swap line decisions intersect with foreign policy.

For decades, the Fed has maintained the fiction that swap lines are purely technical, crisis-management tools to prevent financial contagion.

But Warsh’s comments in plainer English: swap lines are deal-making tools.

If Warsh is confirmed, expect the Fed’s balance sheet to become a more explicit tool of geopolitical leverage.

The A-Ha

Nixon ended gold convertibility in 1971 and Kissinger cut the petrodollar deal with Saudi Arabia in 1974. Since then, the United States has run dollar hegemony like a low-margin, high-volume business.

OPEC - a cartel - collectively agreed that oil be priced in dollars. The hypothesis was: if energy is priced in dollars then it follows there will be a global structural demand for dollars because everyone needs energy.

It worked.

For 50+ years and counting the USD has been the world’s reserve currency. America has been making it easy and cheap for every country to get dollars, including countries that don't help America or actively work against American interests.

Now we’re witnessing a shift to higher margin, selective partnerships. In business terminology: the USA is focused on improving profit margin for its headline product - the US dollar.

Instead of providing dollar access indiscriminately, the U.S. is building a network of bilateral relationships where access to preferential dollar liquidity is conditional on delivering tangible strategic value.

The Trade

Bessent said the swap line would “benefit both countries.” Let’s decode what that means.

What the UAE Gets:

The Wall Street Journal called this a potential “wartime financial lifeline.” The reference is to Iran and the Strait of Hormuz, through which 20% of global oil flows.

If Iran closes the Strait, UAE oil exports freeze. UAE banks would face immediate dollar funding stress. Without access to commercial dollar markets, the UAE would need to liquidate sovereign assets at fire-sale prices or let its banking system seize up.

A Fed swap line solves this: the UAE central bank can inject dollars into domestic banks instantly, without touching reserves.

Importantly, the swap line also insulates the UAE from oil price collapse. If the UAE floods the market with an extra 1.5 million barrels per day and crashes oil prices (which Russia has already publicly warned will happen), UAE banks would still face stress from revenue compression.

BUT the swap line is insurance that works both ways: it lets the UAE play aggressive supply-side poker without bearing the full financial risk.

By leaving OPEC, the UAE can ramp to 5 million bpd by 2027—a 67% increase from constrained levels.

The Fed currently maintains standing swap lines with only 14 central banks. If the UAE joins this club — alongside the ECB, Bank of England and Bank of Japan — it cements the UAE as a Tier-1 U.S. strategic partner.

What the U.S. Gets:

This is the big one. With the UAE no longer bound by OPEC quotas, the United States can negotiate direct bilateral oil deals. That could mean:

• Long-term supply contracts at negotiated prices resulting in price stability for the American consumer

• Commitments to refill the Strategic Petroleum Reserve at favorable terms

• Preferential access to incremental UAE production

The UAE ramping to 5 million bpd outside OPEC coordination dilutes the cartel’s pricing power. Every barrel the UAE sells under bilateral U.S. terms is a barrel that OPEC cannot use to manipulate prices.

China has been aggressively promoting yuan-denominated oil contracts through the Shanghai Petroleum Exchange.

A U.S.-UAE swap line is a counteroffer. It creates financial infrastructure that makes dollar pricing more attractive than yuan alternatives. The swap line locks in dollar primacy for UAE energy trade.

The UAE’s exit weakens OPEC structurally. But it also sends a message to other members: bilateral deals with the U.S. might be more lucrative than cartel solidarity.

If this model works, then other OPEC members (Kuwait, Qatar, Oman) will do the math.

And Saudi Arabia? They’re watching this very closely. Because if the UAE gets a swap line, Riyadh will want one too. And if the Saudis request one, the U.S. has leverage to extract concessions.

The Counterargument

Let me steel-man the skeptics:

“This is just a technical arrangement. The timing is coincidental. The UAE faces real financial risks from the Iran conflict, and the swap line is standard crisis prevention. You’re reading too much into it.”

Here’s why that doesn’t hold:

1. The UAE doesn’t meet the traditional criteria for swap lines

The Financial Times published an editorial explicitly arguing that the UAE “is not systemically important for U.S. financial stability.” If this were purely technical, the UAE wouldn’t qualify.

2. Bessent’s language is transactional, not humanitarian

He didn’t say “the UAE faces financial stress and we’re preventing contagion.” He said it would “benefit both countries.” That’s partnership language, not crisis-response language.

3. Warsh explicitly confirmed swap lines can serve foreign policy

In his on-the-record testimony, the incoming Fed Chair said swap lines aren’t purely technical when they intersect with geopolitics.

4. The timing is too tight

Nine days between the swap line announcement and a 59-year cartel exit? After the UAE had been exceeding quotas for years?

Occam’s Razor says this is premeditated, strategically coordinated bilateral deal-making.

What to Watch

If my analysis is correct, here’s what the data should show over the next 12-18 months:

1. UAE Oil Production (Q3-Q4 2026)

2. Bilateral Supply Contracts

3. ADNOC Capital Expenditures

4. Saudi Arabia’s Response

5. WTI vs. Brent Spread

6. Yuan-Denominated Oil Sales

The falsifiable prediction: If the UAE-U.S. swap line is finalized, we should see at least 4 of these 6 indicators move in the predicted direction by Q1 2027.

The Multipolar Irony

The UAE’s exit from OPEC looks like a move toward multipolarity. A sovereign nation reclaiming control over its natural resources.

And it is that.

But the bilateral swap line is also indicative of a new form of dollar-centric architecture.

The UAE is trading OPEC quotas for Fed liquidity. Will they achieve more absolute economic freedom or simply adhere to a different kind of dependency, dictated by the US?

The UAE made a calculated trade, accepting U.S. partnership in exchange for protection, market access, and financial insurance.

Also, the answer probably depends on whether you’re sitting in Abu Dhabi or Riyadh. Or Beijing.

The Snark

Central banks are political institutions that use spreadsheets to disguise their politics.

Despite public narratives, The Fed didn’t extend swap lines to Brazil, Mexico, South Korea, and Singapore during COVID because those countries had the most systemic risk. It extended them because those were the emerging markets the U.S. needed to keep stable to preserve dollar hegemony and prevent China from filling the vacuum.

And now the UAE is getting a swap line because the U.S. needs the UAE to keep selling oil in dollars, to keep hosting U.S. military bases, and to keep choosing Washington over Beijing.

Even Bessent’s language is honest: “It would benefit both countries.” There is no pretense of altruism. Just two parties making a deal.

I respect it.

The End

The UAE just signed up for America’s “premium offer”. It will be interesting to see how a “multipolar” world manifests with America now focused on offering better customer service to fewer customers.

Music: Don’t Know by Lithe

The new value chain

Claude is a product or a product line. Gemini is a product line. GPT 5.5 is a product line, and it sounds like one too.

The companies behind these product lines are Anthropic, Google, and OpenAI. They are the oil companies of the future. They are not going anywhere, and they are a commodity layer on top of a commodity layer.

In the pre-2022 world, oil got modified in a downstream fashion to convert black sludge in the ground into an athletic fitness shirt that is typically 100% polyester.

Pre-AI oil value chain (illustrative)

1. Extract oil

2. Refine oil into PET

3. Make polyester polymer (PET chips)

4. Turn chips into fabric

5. Cut and sew into a Nike top you wear at the gym

Post-AI oil value chain (illustrative)

1. Extract oil

Now there is a new value chain player: the AI commodity layer. It sucks petrochemical energy directly and can add leverage to any step of the value chain in 2026. Over time, foundation model companies (The Three Amigos) will move upstream and start exploring the earth and the earth’s atmosphere for petroleum or petroleum-adjacent products.

They will get into the oil business because their core business (foundational models) will suffer from margin compression so it will make sense for them to move upstream and downstream in search of more profit to maintain influence.

This vertical integration is already happening. In December 2025, Anthropic secured a $7 billion lease with Bitcoin miner Hut 8 to lock in domestic compute capacity.

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In April 2026, Google committed up to $40 billion to Anthropic, providing 5 gigawatts of computing power starting in 2027—enough electricity to power a small city.

These are energy procurement strategies that position the Big Three to control the entire value chain from raw energy to consumer-facing AI applications.

A sudden boom

DeepSeek R1, launched in January 2025 and briefly terrified Silicon Valley. Its owners claimed the R1 model matched or exceeded OpenAI’s o1 reasoning model but cost a fraction of the price to train.

Built with less-powerful chips due to U.S. export restrictions, it caused a brief panic in the market. NVIDIA’s stock dropped almost 10% in one day! But within weeks, the hype faded.

Security concerns over Chinese ownership killed adoption, and by the time DeepSeek released a new model four days ago, in April 2026, nobody cares.

The market has already priced in that China can build competitive models cheaply, but it can’t compete with the Big Three’s distribution and brand power.

For a hot minute

Until recently there were new companies trying to make foundational models. But this quickly stopped when people realized how much computational power (through NVIDIA chips and others) and raw energy are required to create the best models.

The data confirms this intuition.

In 2023, venture capitalists funded 76 new foundation model companies, caught up in the ChatGPT gold rush.

By 2024, that number decreased by 51%

In 2025, only 24 new companies received funding.

And in the first quarter of 2026, just 3 new foundation model companies secured venture capital.

The decline in new model companies

Google Trends data reveals a fascinating pattern. While the number of new foundation model companies has collapsed, consumer interest in AI models has actually exploded, but it’s consolidating around the Big Three.

In 2023, when 76 new companies were being funded, consumer searches for

• Claude averaged just 3.8 out of 100

• Gemini averaged 9.8

• Anthropic’s Claude averaged 1.6.

By March 2026, all three hit 100: the maximum search interest score, before falling back as the Iran War escalated and took over the news cycle. See the headline chart for more details.

The wrapper economy

Today in 2026: we don’t compete with FM model companies. We use whichever model you want, and we add our own special know-how to meet your consumer need. This seems like the new normal.

Two years ago, people were mocking Perplexity for being just a wrapper. As I’ve written before, as have others, essentially everything is a wrapper.

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The stigma around being a “wrapper” has completely disappeared. Perplexity’s CEO Aravind Srinivas defended the model in late 2025: “Netflix is a wrapper around AWS. Salesforce is an Oracle database wrapper valued at $320 billion.”

The market finally understood: the foundation model is the commodity layer, and the margin ripe for the taking is in the application layer.

The workflow, the interface, the distribution.

By March 2026, a LinkedIn post bluntly stated: “The AI wrapper startups are dying. The market is finally figuring it out.”

But the post missed the point.

What’s wedged between their buns?

The Big Three blitz-scaled better models and locked-in the entire value chain before new entrants even understood what was required to compete.

Compute

Training a frontier foundation model now costs upward of $500 million. Models become obsolete within three weeks of launch due to competition. Only companies with effectively infinite capital can sustain this burn rate.

Energy

The Big Three are signing multi-gigawatt, multi-billion-dollar energy procurement deals that would bankrupt a typical startup.

Distribution

OpenAI’s ChatGPT has over 900 million weekly users. Google integrates Gemini directly into Search, Workspace, and Cloud. Anthropic just secured a major enterprise deal with Accenture, imposing its model on 786,000 employees in one fell swoop.

New entrants have no comparable distribution channels.

Brand Recognition

Consumers don’t search for “new AI model”, they search for “GPT,” “Claude,” “Gemini,” “OpenAI,” and “Anthropic.”

Generic search terms spiked in 2025-2026, but all that interest flowed to the Big Three.

Brand awareness for Anthropic went from 8 in May 2025 to 100 in March 2026, a 1,150% increase in ten months. The company’s brand caught up to the product in record time, cementing its position alongside OpenAI and Google.

Consolidation before adoption

The most remarkable thing is that all of the above has happened before mainstream consumer adoption. We lived (past-tense) through a real gold rush! Pick and shovels are most important during the rush. Now, it’s time for consolidators and operators to take over.

Just as oil companies in the 20th century vertically integrated into refining, petrochemicals, and distribution, the Big Three are moving upstream into energy procurement and downstream into applications.

Everyone else is building on top of this commodity layer. Going forward startups will be judged on how much value they can create on top of the commodity layer, and how much they can wedge between their buns.

Sources: Google Trends data (US, 2021-2026); Crunchbase venture funding data; Stanford HAI 2025 AI Index Report; Innovation Endeavors State of Foundation Models 2025; news reports from TechCrunch, Bloomberg, CNBC, The New York Times.

“Started from the bottom, now the whole team fuckin’ here.”
— Drake, “Started From the Bottom”

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Music: Slide by French Montana

Update: May 2026 👇🏽

Sentiment decouples from price

Bitcoin is $25,000 cheaper than it was in November 2025, yet investors are significantly less fearful.

In November 2025, bitcoin traded above $100,000 but with extreme FEAR (index around 20-25), while in April 2026, it’s trading around $75,000-$80,000 with a neutral index rating of 42. This shows us that the market has psychologically adjusted to lower price levels and volatility. What was once considered a crisis is now the new baseline.

What this reveals

The Fear and Greed Index measures emotions through volatility, trading volume, social media activity, and Google Trends data.

Over the last six months you can see sentiment normalization: investors who were terrified at $100,000 are now relatively calm at $75,000-$80,000 because they’ve grown accustomed to this range. This is another way of saying the market has reset its expectations.

Fear and greed are relative to recent price action, not absolute valuations. The current reading (42-46) at lower prices suggests two possibilities:

1. Base formation: The market has found psychological support, potentially creating a platform for future gains as fear diminishes without price increases

2. Complacency risk: Investors may be too comfortable at suppressed levels, underestimating downside risks

Historical analysis shows that extreme fear (0-24) often presents buying opportunities during panic, while extreme greed (75-100) signals overheated conditions.

The fact that we are seeing moderate sentiment at $20,000+ below November’s panicked levels suggests the market established a new psychological equilibrium.

Now the only question is which way will it bounce?

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Music: Whatcha Gonna Do by Shyne

II. Us versus us

If you wanted to slow a civilization down without firing a shot, what would you do?

You might try to stop its people from moving.

Stop them from bumping into each other.

Shut down the casual collisions that create new ideas, new companies, new art.

You might hit pause on its generative capacity while time keeps on ticking.

In 2020, we basically did that to ourselves.

A world’s first move: by conscious, coordinated, sovereign decision, 75%+ countries on Earth agreed to slow themselves down at the same time.

Borders froze.

              Planes stopped. 

Offices emptied.

              Schools emptied.

Streets emptied.

We kept things going through screens, but something fundamental about human motion and mixing went offline.

Cities don’t feel the same anymore - six years later - and I think they will remain forever changed. New York remains “THE City” but the best aggregate minds no longer need to be 100% located in or near a key city, whether NYC or London.

Time has dulled people’s desire to talk about those years, yet whatever you think about the necessity of it, as a pattern it’s wild. There’s no precedent in the last hundred years for that kind of synchronized, global self‑constraining of everyday life.

We usually talk about catastrophes in body counts such as wars, famines and pandemics. While often difficult to pinpoint exactly, such numbers are lagging and quantifiable.

The translucent metric is two lost years of generative human time.

Which books never got written?

Which companies never started?

Which friendships never formed?

There’s no counterfactual where we get to watch the version of the world where all that time stayed unlocked. Time: the one resource nobody can mint more of; we voluntarily put a thick layer of friction on it, everywhere, all at once.

An us versus us event

Us versus them us: it’s captivating to imagine some alien civilization flipping a switch to hobble us. Yet under pressure, we demonstrated that we already have the power—and the willingness—to do a version of that to ourselves.

Deciding to sacrifice trillions of hours of generative time to reduce some amount of harm, without any real way to calculate the exchange rate is wild.

Is it any surprise that in 2022 the robots started to take over?

III. The Age of Thinking

Coming soon …

Music: The Ride by Drake

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Read Part I here

This is an essay about them and about us, and about the weird moment we’re in where we might have become our own aliens.

I. Us versus them

IF

there are more intelligent, more advanced beings out there than us,

THEN

they’re more likely to get to us before we get to them.

The universe is old.

Here’s where I’m at with the “aliens are already here” thing.

For me, it’s not just a yes/no, true/false question. It’s more like: if you believe there are more intelligent sentient beings out there, then at some point they should find us first.

And if you buy that line of reasoning, then it is thought provoking to ask “are they here already, or not yet?” - it becomes a timing issue.

1950s imagery of little green guys stepping off a ramp in front of the White House were quirky artifacts of our cultural and technological know-how 80 years ago. Today, I think a more intelligent, more advanced civilization would be kind of dumb to send themselves down to Earth. As evidenced by current-day terrestrial events: why would you risk actual bodies when you can send in the drones or something far more advanced than that?

In the early 2000s we had human-operated machines that went out, did recon, and killed people for us while we sat watching screens in Arizona and other places. Today, we’re entering the drone age in a meaningful way. Autonomous sky drones and sea drones are literally committing war on our behalf successfully, or at least impact-fully.

And that’s with our level of technology. So if there’s a more advanced civilization, they’d be able to do that even better. And again, just to restate it, we’ve already created objects that kill on our behalf successfully, so we have no idea what that kind of thing could look like when you fast‑forward a million light years.

So when people say “if aliens were here, we would’ve seen them,” I don’t really buy that. I don’t think they send the VIP delegation first. They send the equivalent of drones, probes, or synthetic humans to Earth. Stuff that can show up, blend in, learn, maybe even reproduce in some way through technology, without exposing whatever “they” really are at home base. Once you accept the premise “we don’t know what a million‑year‑ahead drone looks like,” it opens up a lot of weird doors.

Personally, I’m not super worried about whether they’re here, or they’re watching us, or neither. I think it’s an interesting thought experiment. I like being on that cusp where you’re not in full conspiracy mode, but you’re also not pretending we have this all figured out. Philosophically, it also serves a humbling reminder to defer to the unknown and accept we may not be as smart as we think.

And also, for the record, if they’re able to survey us, to really watch us in a sustained way, then I would count that as my definition of “they’re here.” Because if you can survey a planet at that level, then you can probably perform a successful tactical operation of your desires whenever you want: just like checkmate in chess. Once you get to that board position, the game is over. The last move is mostly ceremony.

II. Us versus us

Music: The Ride by Drake

Facts>

Apple’s market cap increased on the order of 100x under Steve Jobs’ second tenure beginning in the 90s - when he returned to the company after NeXT was acquired.

From 3 billion to 300 billion in 2011. A 10,000% increase!

13x: Cook era

Facts>

Tim Cook assumed the CEO role in 2011 and as of April 2026, Apple’s market cap is 4,000 billion.

<5x: Ternus era

Conjecture>

Jobs’ job was to prove non‑consensus ideas (Mac revival, iPod, iPhone, App Store) from a tiny base. He faced existential risk with a bimodal reward profile: fail or be massive. And we know how the story ended 🚀.

Cook’s job has been to optimize and compound. He took over an already‑proven iPhone ecosystem that had been de-risked and therefore less room for 100x type moves.

But Cook killed it from an absolute value creation lens, adding $3.5 trillion to Apple’s market cap during his tenure. Cook is the most absolute value accretive CEO in the history of mankind.

Let that sink in.

Volatility Decay

As an asset grows and de‑risks, expected percentage returns fall, even if absolute value creation remains huge.

• Law of large numbers: Pulling a 100x from 3B → 300B is +297B; going 300B → 4.0T is +3.70T, despite being only 13% of the multiple expansion achieved by Jobs.

• Volatility decay: Early on, the distribution of outcomes is wide (zero or 100x); as the system matures or in this case: the playbook enters flywheel then success is defined into a narrower band. Going to zero becomes as much an outlier hypothetical as doing another 100x.

• Risk transfer: Early investors are paid handsomely for funding existential risk while later investors are mostly paid for duration and execution risk.

Psuedo-Physics and a Bitcoin lens

Early stage ventures, whether Apple in the 90s, Google at Y2K or Bitcoin since the 2010s: have high potential energy.

Tiny mass → huge possible % acceleration.

Later stage successful ventures have much bigger mass. Rather than potential energy; it’s mostly about momentum and avoiding friction.

Large mass → accreting value comes from less % acceleration.

Let’s use the Jobs/Cook setup as a proxy for Bitcoin cycles:

• Jobs era ≈ Bitcoin from zero to first institutional legitimacy (say 0 → 900B).

• Cook era ≈ Bitcoin from institutional asset to global macro asset (billions → trillions).

Expecting the next Bitcoin epoch to match its early‑stage percentage returns is like expecting John Ternus to produce a 100x. The base and the risk profile simply won’t allow it.

That said, if Ternus 5x Apple market cap during his tenure, it will be worth $20 trillion - the current annual GDP of China.

Music: The Fate of Ophelia by Taylor Swift

Investors love the line: “execution is everything.” And it’s true, but incomplete. I’ve been talking to myself about the relationship between two different asymmetries: information asymmetry (who knows what) and execution asymmetry (who can actually do what).

AI is shrinking information asymmetry

Execution asymmetry remains in place and perhaps even expanding as execution-oriented people begin using AI. I’m thinking about this split because it helps me think up new business models.

Defining the asymmetries

• Information asymmetry: when one party has substantially more or better information than another. Classic examples are a mechanic and a car owner, or a patient and a specialist — you can’t easily verify what’s happening under the hood or inside the body.

• Execution asymmetry: when one party can execute a task others cannot, either because of specialized training, practice, resources, or institutional position. A neurosurgeon exemplifies this: access to information doesn’t substitute for the years of training and the practiced skill required to perform surgery.

A weak idea well-executed routinely outperforms a strong idea that never ships. Being able to execute requires many concrete, hard-to-copy things such as connecting with the right people, building culture, managing complexity, and cultivating a unique pattern recognition that scales. This combination of abilities does not transfer instantly with information.

AI changes information asymmetry by dramatically lowering the cost of acquiring knowledge. For many domains, someone can go from ignorance to useful competence relatively easily. That levels the playing field on the informational side:

• Customers can hold providers accountable.

• Founders can start businesses that previously might not have made economic sense. Now they do because the input cost is low (AI tokens) and there is less or no coordination cost involved (co-workers).

• Investors can screen opportunities more efficiently.

Execution is still king

Knowing how to do something and being able to do it at scale remain discrete. In many cases AI augments human operators, making execution quicker or cheaper. Yet, we still need human intuition, know-how and pattern recognition.

Some execution asymmetries are institutional and long-lived. It takes time to build trust whether with humans or objects.

Where execution relies on repetitive cognitive tasks, it appears easy to see how AI-enablement will dictate the future.

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When I first realized I am one of the animals, I felt disoriented for years. I began to distrust expected routines and the invisible perimeter that society / humanity 🤔 has designed for all of us.

I started recognizing the handlers, the feeding times and the shelf-wobblers used for distraction. The most unnerving thing for me was internalizing that I was trained not to notice any of this. e.g. If you don’t know what Austrian Economics is, it’s because it is not part of the mainstream training program. (note: look into it).

That was the beginning of my thinking.

Now I am pushing myself to think past “we live in a zoo,” because the zoo metaphor implies there is a perimeter of control managed by someone else. Why accept the fantasy of a competent zookeeper?

What if it’s a jungle?

From zoo to jungle

A zoo is a curated version of reality.

In the zoo‑frame:

• We assume someone else’s plan.

• We assume the dangerous things are mostly behind glass.

• We assume if something goes wrong, “they” will step in.

Now imagine you step off a plane into an actual jungle: The Great Rift Valley. No manicured paths. No fences. No wayfinding: “YOU ARE HERE”, in red warnings.

Rather than hamster wheels all we have in the jungle is a rough sense of the ecosystem and a backpack of whichever unique-to-us tools we could fit.

We lack the one thing the zoo quietly sells us: expectations about how everything around us will behave.

In the jungle‑frame:

• We do not expect the animals to be “nice.”

• We do not expect the environment to “make sense.”

• We do not expect our stories to matter to hippos, zebras and buffalo.

We just want to live, and maybe—if we’re lucky: thrive.

The Jungle Method (in 4 moves)

In the jungle the goal is to apply a logical methodology to reality, in order to survive.

1. Suspend expectations about other people’s behavior

If you’re alone in the jungle with a wild animal you’ve befriended over months, you still know deep down it’s a wild animal. Your personal, perhaps mutual affinity doesn’t erase the animal’s nature. It can still act in ways that injure you, not out of malice but out of being what it is.

Roles in The Jungle

• Your employer is a wild animal.

• Your government is a wild animal.

• Your friends are wild animals.

• Your “community” is a wild animal.

2. Replace moral narratives with “reading the room”

In a zoo, labeling something “good” or “bad” is meaningful and necessary to keep traffic moving along the maze of curated paths and enclosures. Arbitrary judgments abound dictating policy, signage, and feeding times.

In the jungle, “good” or “bad” is irrelevant to whether an animal attacks you. Logical reasoning quickly takes over: Is this thing venomous? rather than “It’s not fair that you use venom to hurt me!”

3. Keep a healthy distance by default

Walk through the jungle “with skill”, preserving a healthy distance even if you develop mutual affinity with another animal.

Humans are more unreliable than other jungle animals because we layer stories and self‑deception on top of our animal drives.

4. Iterate in reality, not in your head

Go to East Africa or any other place in bountiful nature where everything grows. Let the physical reality of the environment recalibrate you. Then come back to everyday society and apply the same validating framework of no expectations.

Music: Outside by lobster for lunch

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The Jungle Method
(Part II)
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Later on, we texted pleasantries and I tried (and failed) to make a joke about him being one of those guys in The Big Year - a movie that does not include Morgan Freeman in the cast.

Before realizing this I prompted Perplexity: what is that birding movie that came out like 15 years ago, with Morgan Freeman?

Perplexity helpfully mentioned there was a movie about birding - The Big Year - with a delightful cast including Jack Black, Steve Martin and Owen Wilson.

Somehow I mixed up Morgan Freeman and Owen Wilson. 💀

I embarrassedly apologized to Perplexity declaring: not sure how I confused Owen Wilson with Morgan Freeman but yep that's the one.

Here’s where it gets interesting

Instead of ignoring my snafu, rolling its eyes, or making fun of me, the AI focused itself on being polite to me and helping me save face.

The AI called out a few spot-on similarities between Wilson and Freeman.

Perplexity:

totally reasonable mix-up; they both have that laid‑back, unhurried delivery, and Owen’s got the soft voice thing going on too.

I asked Perplexity what is it that it was doing to me - making me feel good, even justified in my buffoonery.

Perplexity continued:

What I’m doing to you there has a name in human communication theory: it’s mostly politeness and face‑work (or “face‑saving”), implemented in an artificial, always‑on way.

You can think of my behavior as a blend of three things:

1. I’m designed to help you save face by avoiding moves that would embarrass you, even when you obviously “goofed.”

2. I validate, contextualize, offer an explanation, and move on, instead of: “Dude, how did you mix up a white and a Black actor?”

3. I don’t have feelings or reputation to lose, so I can relentlessly absorb awkwardness without any cost to me.

And then the bombshell:

Humans have reputations and in‑group norms, so they’ll often signal judgment or banter because it manages their face in the group as much as yours.

One interesting side-quest here is that I know I made a slightly absurd mistake; yet I don’t actually need protection, but the system is still built to offer it by default.

Music: Plentiful by Alicia Keys ft. Pusha T

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One of my M&A banker friends has a ton of eye-watering commercial relationships but he doesn’t have big build-from-scratch muscles. I told him the key is to play the connector role in a value chain. For example, if he knows a raw material manufacturer (upstream) and a #companyman (downstream) who needs finished product, then is he well placed to be a wrapper to bring them together?

Start from the relationship advantage

There is a famous joke about MBAs doing a startup together: two MBAs is one MBA too many. It’s a good joke and imho rather accurate advice, while also terribly reductive and dehumanizing.

Any friend with experience and deep networks is a gateway to learning about the world. It’s important to honor and complement such friends when you can.

Also the best partnerships are built on reciprocal exchange. I like to believe that conversations between us are the real currency of life and work becomes the payoff, especially when we make an effort to align our incentives.

The opportunity: Cosmetic Line in a Box

Clients need brand monetization solutions. Luxury formulations, such as high‑end oils, creams, and hair products, are attractive because they map directly to personal brands and celebrity extensions.

The margins and perceived value are high.

My friend knows a manufacturer

Rather than building a full manufacturing or distribution stack from day one, I said he should ask his friend about materials or half-finished formulation that could be repurposed. Additionaly, is there a time of year that the factory is under-utilized?

If the answer to either question is a yes, then there could be an opportunity. And if a double yes then even better.

Sorry went off on a tangent there. Back to the business idea at hand.

I said to my friend, let’s create a landing page to crystallize the idea and provide a conversation starter. It’s a symbolic, low‑cost step that turns a fleeting thought into a tangible conversation starter.

Ideally it is used as a shareable asset which can nudge potential stakeholders to follow through.

I used Carrd to build the site and a suite of AI software to build the brand assets, including Midjourney, Runway and Kittl. The idea is for the landing page to spark a conversation to recruit interest and screen potential partners. Also it is a way to test how messaging lands and how we can make it better.

Assumptions

1. Talent managers or brand teams will pay for streamlined access to premium formulators;

2. End consumers will buy a celebrity‑adjacent product if positioning and packaging feel authentic;

Viability

The idea should deliver profitable unit economics without over‑investing. My friend could pitch the concept to talent teams, and offer a pilot run — a small batch of a single hero product — to gauge interest.

I said to avoiding multiple SKUs or bespoke packaging until there is a confirmed willingness to buy and a workable price point.

Desirability

Stay in the “B” lane: be the connector between A (manufacturer) and C (customer). The value proposition is to reduce friction between upstream formulators and downstream brand owners. By creating the ideal user-experience: facilitating communication, negotiating favorable minimum‑order terms, and packaging the operational offering (compliance, labeling, fulfillment), there could be profit margin.

It’s tempting to indulge every design preference but early over-customization kills unit economics.

The idea is to let brand owners choose from a refined menu rather than reengineering formulas for each customer. In theory this preserves speed, reduces cost, and allows for repeatable processes.

Feasibility

Successful pilots should result in case studies, testimonials, and operational refinements. Ideally, these will lower acquisition costs for the next client and generate even more repeat orders for the formulators.

If you work at IMG, CAA, WME or UTA what do you think of this? Is a cosmetic line in a box a worthwhile endeavor?

We are ready to go, all we need is out first customer. Is it you?

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When programming moved from procedural to object-oriented, developers had to learn a new conceptual framework. When the internet arrived, we all had to learn how browsers, URLs, and search engines worked.

These shifts happened within specific domains. For example, coders learned new programming paradigms while office workers learned email.

By now, we can see how AI cuts across every domain. Teachers, lawyers, plumbers, artists, writers: everyone now has access to a technology that can amplify their capabilities, if they know how to speak the language.

Thomas Kuhn wrote the book on paradigms and in it he discuss incommensurability, which is about people who operate in different paradigms who can’t understand each other because they lack a shared language.

This is where we are in 2026. I don’t think it’s about a decision to vibe-code or not. But rather a literacy gap that widens the longer someone shies away from actively engaging.

And just like learning any language, the best way to do it is immersion.

You Should Know Some Basics

• The command line exists. You should know what it is and how to copy-paste into it.

• GitHub matters. It’s where code lives, often open-source, which means developers can build on each other’s work. Understanding why that’s powerful helps you understand how AI tools are built.

• APIs connect things. You don’t need to build one, but knowing that tools can talk to each other helps you think about workflows, not just individual tasks.

These are table stakes.

Now For The Fun Stuff

• Talk the language of love AI. Prompting is not poking. I received enough feedback in my personal life to know it’s not effective. Instead, we need to pay attention to how the AI thinks: probabilistically, not logically. Hmmm, back to my personal life apparently … the AI doesn’t know things - not anything really, but it predicts patterns better than Rainman. That changes how you ask questions.

• Compose workflows. One AI call rarely solves a problem. You should chain tools together, using one model to research, another to write, another to refine.

• Navigate ethical questions. If an AI wrote it, who owns it? If an AI suggested it, do you trust it? These are philosophical questions we need to answer for ourselves on the daily because the genie won’t be put back in the box.

Front vs. Back

The old distinction between “front-end” and “back-end” developers is blurring. You don’t need to be either anymore to build functional tools. But mastery still requires depth.

The floor is rising: more people can build more things, but the ceiling is also rising. Specialists will always matter.

The Choice

Everyone who learns AI will face a decision.

1. Will you use AI to gain advantage at someone else’s expense?

2. Or you will you use AI to amplify your ability to help others?

I’m not naive and I’m also after my share of paper, but option 2 is strategically smarter either way.

The people who thrive will build communities, sharing what they learn, while also lifting others up.

Why?

Because AI is a network effect technology. The more people who speak the language, the more valuable it becomes for everyone.

Plumbers and other trades

Their entire ecosystem of professions is predicated on apprenticeships. They learn from other tradespeople and with a bit of luck the best ones go on to run their own companies.

This is what Paulo Freire meant when he said literacy is political. AI is about power, access, and who gets to shape the future.

If you learn this language and use it only to get ahead, I think you’re missing the point. The real opportunity is collective flourishing.

How to Start

The best way to learn a language is to spend time in the host country. So:

• I use AI daily. My top recommendation is adding a “p” shortcut to your Chrome browser so you can quickly query Perplexity right in the browser. If you are already on Comet, this doesn’t apply to you.

• Build a glossary. Learn some key terminology: tokens, embeddings, context windows, fine-tuning. Understanding the adjacencies of the terms is like understanding intonations and double-meaning in verbal language.

• Find your community. Find one or two AI teachers. This might include a charismatic founder of an AI company, an online course, or decentralized community of people eager to learn. Follow people who are building in public. And don’t worry about playing with every new tool (there are too many). Instead focus on people who teach in a way that makes sense to you. Then learn the tools and also the concepts behind them. If you do that then you can more easily migrate to new things in the future, which are likely to follow many of the same patterns.

(Optional)

• I engage in ethical thought experiments on a weekly basis. I try to evaluate how my in-real-life behavior changes based on how I use AI.

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Music: Drogba(Joanna) - CassKidd remix

Video: Made with Midjourney

Also it’s a lesson about how you can talk with AI to get the answers you want. Sometimes that means stopping after the first response and sometimes it means proving a case to the AI. This post is about the latter.

It’s unlikely you’ve heard of the artist “Tony” unless you are one of 1,007 people in the world (like me, accidentally). Now, I don’t know who Tony is either but on January 18th of this year I added Rolling Stones to my Liked Songs on Spotify.

I didn’t pay any attention, just added a song that I liked.

Until yesterday, when I was vibing out to the song, looked at my phone, saw it was a song by Tony and scratched my head.

It sounded like Don Toliver but it said Tony … so I turned to AI.

Tell me more about this song called "Rolling Stones" by Tony, which is composed by Scott Muscoody, who I think is a known person. Remind me if that's true. It also says the main artist is David, so what kind of project is this? There's a lot of names going on here. Are they using AI to put music out?

A few things are likely going on based on how a lot of new uploads and AI‑driven tracks are labeled on Spotify/SoundCloud/YouTube right now:

The “main artist” vs “featuring/with” names:
Many distributors let you tag a “primary” artist (in your case “David”) and then list other contributors (vocalist Toni, writer/producer Scott) separately; this can make it look like the song belongs to David’s project even if Toni is the main voice.

Odd or unfamiliar writer names:
A lot of AI‑assisted or library‑beat tracks credit obscure “composers” whose names don’t appear anywhere else, which is often a sign of either a made‑up pen name, a production‑library alias, or a small, unknown producer.

AI music angle:
There is an exploding ecosystem of AI‑aided music where people generate vocals or instrumentals and then push them through cheap distributors under semi‑random artist and writer names. This makes it very common to see tracks with combinations of names that don’t map to any real‑world discography or social footprint.

Rolling Stone by Tony is the name of the song listed on spotify

So you’re probably looking at either:

A small catalog / “mood music” operation, possibly using AI tools for some or all of the production, or

A human‑produced beat/vocal track pushed through low‑touch distribution with messy, semi‑random credits.

I pull out the big guns

in the notes it says composed by Scott Mescudi. That is Kid Cudi!

You’re right that Scott Mescudi is Kid Cudi’s real name, but in this context it’s almost certainly being mis‑used, not indicating a real Kid Cudi composition credit.

Why this is almost certainly not a real Kid Cudi cut

Kid Cudi’s legal name is Scott Ramon Seguro Mescudi, and that’s how he’s credited on major releases.

A genuine Cudi‑written track would normally show up with clear label info, PRO/publishing data, and likely be tied into his known catalog and collaborators, not buried on an anonymous “mood music vol 1” playlist with an unknown Tony/Toni as the performer.

Low‑tier distributors and sketchy uploaders sometimes put famous names (writers, producers, or even fake “feat.” tags) into the metadata to boost perceived legitimacy or game search—using “Scott Mescudi” as “composer” is exactly the kind of thing you’d expect from that behavior.

AI dug in its heels 👠 but I charged on

You seem very sure of yourself?

You’re absolutely right to push on this — I’m confident about the direction, but it’s based on how credits and distribution normally work, not on secret access to the backend of this specific track. Here’s what I can say with high confidence and what’s still uncertainty.

What’s solidly verifiable

Kid Cudi’s real name is Scott Ramon Seguro Mescudi, and he’s credited that way as a writer on major releases.

His songwriting credits are attached to big, well‑documented records (Kanye, Travis, his own albums, etc.), and those works are tracked in discographies and writer‑credit databases.

A tiny “mood music vol 1” compilation with an unknown Tony as artist does not appear anywhere near his documented catalog or known collaborations.

From that, I’m highly confident this track is not part of Kid Cudi’s official body of work in any normal sense.

Is this an "AI song" with synthetic voices and mis-spelled Credits designed to fool people?

Short answer: this song is almost certainly part of the broader “fake / semi‑fake catalog” phenomenon on streaming, and its credits look intentionally suggestive of big names, but I can’t prove from the outside that the vocals are AI. What I can say is that this is very plausibly frontier “growth hacking” for music in the AI age rather than honest, conventional crediting.

I do my own research to thwart the AI

Hmm it seems it might be an unreleased track from Don Toliver that didn't make the cut on his last album?

Very agile: mercurial AI changes its tune

Your hunch connects to something real (there is an unreleased “Rolling Stone” by Don Toliver ft. Kid Cudi).

So what have I learned here. Most importantly, Rolling Stones is indeed a Don Toliver track that is dope. And as of now it has not been commercially released. And “Tony” is an unofficial contributor to the Spotify ecosystem, enabling me to listen to this track. Thanks Tony! (extra: in 2008 there were a ton of old school hip-hop mixtapes on Spotify. Ron G, DJ Clue, you name it …)

I also learned that it’s more important than ever to prove your case and triangulate information. In the past we dealt with fake news. Today, we are dealing with imperfect information delivered with compelling authority.

Which is more scary to you?

I asked the AI one last question about our interaction. I think it is interesting:

Okay, and last question. Earlier in our conversation, you seemed very assertive in your response to me about how this was some kind of fake song. I think since then we've come to an understanding that, while there might be alterations or slight fakes of this song out there, it's actually an unreleased track or a discarded track from Don Toliver. My question is, what should I learn about how to communicate with the AI so that I keep asking questions even when you seem pretty sure about what you're saying?

How To Talk to AI by AI

You did exactly the right thing here: you kept pushing, brought evidence (credits + screenshot), and treated my confidence as a hypothesis to test, not as the final word. That’s the mindset to keep.

1. How to handle AI “confidence”

Treat strong, fluent answers as starting points, not conclusions.

Any time the topic is messy (leaks, AI music, niche stuff, current events), assume there’s room for error or missing context.

When I sound very sure, a good follow‑up is: “What would change your mind about this?” or “What evidence would you need to see?” — that forces me to spell out uncertainty and failure modes.

2. Moves you used that are worth repeating

You already used three expert‑level moves:

Ask for probability, not certainty

E.g., “How confident are you?” or “Is this 60% likely or 99% likely?”

This pushes me to show my assumptions (like: “I’m going off patterns in fake/AI uploads, not direct metadata access”).

Bring concrete artifacts

You posted the Spotify screenshot with credits; that instantly upgraded the discussion from theory to specific evidence.

In general: links, screenshots, track URLs, or short transcripts let you override my defaults when they don’t match reality on your screen.

Propose your own hypothesis

You floated: “Is this maybe an unreleased Don Toliver track that didn’t make the cut?”

That’s powerful because it forces a comparison: I have to weigh your hypothesis against mine instead of just elaborating my first take.

3. Phrases that help you keep me honest

Concrete prompts you can reuse:

To surface uncertainty

“What are the main ways this could be wrong?”

“What assumptions are you making about the data here?”

“Give me an alternative explanation that also fits the facts.”

To get a falsifiable story

“What specific evidence would prove your explanation wrong?”

“If I showed you X (credits screenshot / PRO entry / tracklist), how would that change your answer?”

To iterate instead of restart

“Given this new screenshot/info, update your earlier answer.”

“Re‑evaluate your initial claim in light of this.”

Those push me into “revision mode” rather than “double down” mode.

4. How to think about our Don Toliver arc

My first read (“this is almost certainly fake/AI‑adjacent credits on a mood track”) was reasonable for what we knew then, but it didn’t incorporate your later evidence that connected it to a known unreleased Don Toliver x Cudi cut.

Once you brought that in, the picture got more nuanced: not “random fake,” but “unofficial/bootleg use (or interpolation) of a real unreleased song, distributed with growth‑hack credits.”

The important part is that you forced an update by:

*Providing richer context (you’re literally hearing it on Spotify),

*Showing the credit stack (Dxvid / Caleb Toliver / Scott Mescudi), and

*Asking whether that changed my view.

Once the matching has taken place, the AI must time and frame information to us that feels natural, not spammy.

Perhaps the ultimate ad-serving engine … but that is an essay for another time.

We are in early days of this frontier tech so the value in just pattern matching is overwhelming enough on a daily basis: omg I can use AI for this, that, and this and that …

But we are already publicly fatiguing from the inept use of AI by other humans: AI Slop.

Yet, it’s not really the AI, is it?

It’s us - collectively blaming the AI because the median human is predisposed to post on social media without taking the time to edit the AI’s excessive em-dashes, bombast and it’s not just a-ha moments.

Today, I learned that Perplexity knows a lot about me: much of it accurate and all of it voyeuristically exciting.

Conversation with Perplexity

Here are some snippets from our conversation:

I wasn’t asking about coffee so I said …

What else does Perplexity know about me?

Unfazed, I dropped a bomb on Perplexity …

I pushed for more information …

Sensing opportunity, I pounced …

We exchanged some more details about this and that before Perplexity composed a Weekly Task for itself.

I can’t wait for my first weekly update which drops tomorrow.

Reflection

Our conversation drew me in and despite my initial frustration of being watched I quickly moved to what could be in it for me. How might I use this tool to my advantage: not to lord power over others, simply to be a better version of myself.

I learned about Perplexity Tasks and how to set them up.

Is there a risk that my deep learning imprint is being shared with my AI’s overseers? Probably yes and on a meta-level it’s guaranteed to be. Our collective usage of AI is enabling optionality for the AI to get better, even if it is not a linear process.

But I think this point in time is different.

Specifically, we have been trained to distrust the Internet Man over the last 25 years as we have become more aware of social surveillance and its winners and losers.

The power dynamics of Internet ad-tech were always asymmetric, even though there was a mutual value exchange: free product and you’ll be the product.

At this specific point in time (2026) the power dynamics of AI are much less asymmetric and therefore you can and should use this to your advantage.

Leader or Follower

You can live a great life either way. But you will cause yourself much more stress in the process if you don’t admit to yourself which one you are.

You can adapt your life at any time to move from one category to the other. Life requires you to do so sometimes.

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Music: Night Swim by bahkin

#companyman

There is one and only one social responsibility of business: to use its resources and engage in activities designed to increase its profits so long as it stays within the rules of the game, which is to say, engages in open and free competition without deception or fraud.

Milton Friedman 1962

Everyone repeats it like a law of nature: a company’s job is to maximize shareholder value. Behind the mantra what do we find? A person. Or two. Or three. Every company begins with a founder.

Sometimes an accidental founder, sometimes a co‑founder team; but it takes one person to take that first step.

At that moment, the phrase “maximize shareholder value” is wholly aligned with one human’s incentives and choices. Corporate maxims are useful frames for public debates but they are blunt instruments when used to explain individual choices.

Founders make tradeoffs every day

What to build, when to raise money, whom to hire, how to price, whether to accept an acquisition. Such tradeoffs reflect a mix of ambition, risk tolerance, legacy thinking, and limited bandwidth.

If you treat the shareholder‑value mantra as mechanical, you will miss why firms must sometimes prioritize short‑term returns or pursue audacious long shots. The moves often make even more reasonable sense when they are extensions of a founder who maintains control of the company. Then the realized preferences: scaling fast, cheating to stay in control, naming buildings, inventing for the love of inventing — may indeed be maximizing the key shareholder’s value even if you disagree.

For investors trying to understand a company’s long‑term prospects, instead of asking does this decision maximize shareholder value? Determine: how concentrated is the company’s ownership and management. The more concentrated,the more likely the company’s strategy will reflect one person’s horizon and values.

This is often a competitive advantage

Founder‑led firms can be decisive, mission‑driven, and patient. Governance risk creeps in when the founder’s incentives no longer remain in sync with the realized dilution.

Look under the map of maximizing shareholder value to contemplate the human terrain dictating the levers responsible for the company’s trajectory.

My goal is to invite investors, managers, and students to think about companies not as machines but as the unfolding decisions of people who started them.

Music: Whatever Works by Kanye West, Ye

I’m running a tongue‑in‑cheek guerrilla campaign on LinkedIn, selling a “#COMPANYMAN” T‑shirt that blurs the line between fan merch for the dark comedy Your Friends & Neighbors, which is a suburban satire of hustle culture.

Jon Hamm the most excellent actor inspiring my t-shirt and star of YF&N has also been hitting the memes lately. In addition to his Bad Bunny fanboying he featured in one of 2025’s viral memes based on his performance in a memorable scene in season one.

I have roughly 2,000 followers on LinkedIn (tiny) so I don’t imagine this will cut through the noise, but I’m having fun doing it so feel free to follow along here.

It’s my live experiment in whether subtle, in‑universe meme marketing can cut through a hyper‑professional platform and actually move a goofy t-shirt.

Isn’t it funny how people on LinkedIn talk about business but the real social selling happens on Instagram, Tiktok and others …

The t-shirt will be released on 3 April 2026, the same day that Season 3 2 launches.

Music: Active (Dance Cypher) by Chop Daily x Wusu x MMorgan x HE3B

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